55 search results - page 1 / 11 » A duality view of spectral methods for dimensionality reduct... |
ICML
2006
IEEE
14 years 5 months ago
2006
IEEE
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenm...
CVPR
2008
IEEE
14 years 6 months ago
2008
IEEE
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
BMCBI
2005
13 years 4 months ago
2005
Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks ...
PKDD
2004
Springer
13 years 10 months ago
2004
Springer
We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certai...
ICML
2010
IEEE
13 years 3 months ago
2010
IEEE
Many clustering algorithms only find one clustering solution. However, data can often be grouped and interpreted in many different ways. This is particularly true in the high-dim...